Help, how can I get them to understand it better?

We live in an age of data, and we are now being told from all sides that we need to integrate more data into our teaching. But if you are like me, your students just don’t even know where to start when working out how to make sense of the data in a visualization in front of them.

Perceiving: What does it show?

This relates to actually being able to read the data visualization (chart). This is a step that many of us as data experts jump over forgetting that our students as data novices often forget to do it. We need to role model and they need to explicitly and repeatedly stop to ask themselves questions that get at things like:

Can I, in general terms, describe what this chart is trying to show me through data?

Can I get a sense of what the data represent?

Can I say what the data representations (e.g., color, shapes, sizes) mean in this chart?

Do I understand the connection between the data representations on the chart and their perceived values?

These are the kinds of things that data experts do subconsciously. Certainly, I am NOT saying ask your students those questions, but it is worth thinking about it if the questions we are asking them lead to their understanding of this or just a regurgitation of what is on the page without any deeper understanding of what it represents.

For example, how many times has a student written in that Time was on the x-axis but then did not talk about the pattern in the data as a change over time? What seems like a misunderstanding in their interpretation may in fact be a lack of understanding of what is actually on the page.

So, we need to make sure they first get what they are looking at, then we can get them to make the next step.

Interpreting: What does it mean?

This relates to our large desire as humans to make sense of things and find patterns (even if they are not actually there :)). Once we know what we are looking at we can start to try to make meaning from it. Kirk suggests that we ask ourselves questions like:

Is it good to be big or better to be small in this context?

What does it mean to go up or go down for these data?

Is that relationship meaningful or not in this context?

Now these are questions that Kirk is putting forth in terms of what a data visualization designer needs to think about when making data visualizations. But, we as teachers helping our students make sense of data visualizations also need to think about how interpretation is a distinct and separate step from data analysis.

How someone makes sense, or interprets, data is influenced by their prior knowledge about the content/context and the kind of data visualization they are looking at, as well as their ability to use that information to make connections to what they know and what they are now looking at in the data.

In other words, this is the step where we make meaning of data points. When we realize that the data points represent values, relationships, patterns. But we have to stop and make sure we are taking the time to understand what the implications of those connections are.

More than just a statement of what the highest and lowest values are, we need to think about the fact that gives us the range of our data and what that range of data could mean for our interpretation of the patterns/relationships in the data.

So, once we know what we are looking at and know what it means as a stand alone. Now it is time to bring it all together.

Understanding: What does it mean to me?

While this may seem a bit personal for science, where we are so often taught to separate our emotions and personal connections to be unbiased and subjective, we learn best when we actively have a connection to what we are learning. This active connection can come from an actual personal experience with the data/context (which is a knock out of the park, ideal) BUT it can also come from just pausing to think “OK, what does this mean to me?”

This is in fact what we are after as science teachers. Right? We want our students to be able to use the data to think about how their new knowledge from the data makes a difference in what they already knew about the context before looking at the data. If not, why else are we having them look at the data?

So, ask them what it means to them. Help move them out of guessing what you want them to say and teach them how to think about what they take away from the data. Have them share these with each other in partners, small groups, or as a whole group. Build a culture of talking about different interpretations and understandings of the data. There is never just one conclusion or claim to draw from data, so let them share. You may be surprised how many take away what you did from the data, and how much they learn from one another as they role model data understanding together.

Looking for ways to better help your students understand the diversity of religions across the world?

One way is to have them dive into the numbers of where different religions exist throughout the world. The World Religion Database, has statistics on religious affiliation for every country of the world. It provides the best estimates for each religion in terms of its international religious demography from 1900 to 2050.

You can access the data through a free trial to explore questions like:

Data visualizations are all around us and our students. And we incorporate data visualizations (e.g., graphs, maps, tables) into our teaching with data. But why do we visualize data in science? How can we create better visualizations? How do we develop our visualizations to tell a story with our data?

Join us each Wednesday as we dive into the world of data visualization and think about different aspects of creating more effective data visualizations.

You want me to add what to my charts?

Depending on what school you teach in or grade level you are working with, you may have come across the somewhat dreaded reality of visualizing variation in graphs.

Any time data are presented in the primary literature scientists visualize the variability in a range of ways, they: plot confidence intervals around raw data, include standard error bars on averages, etc. This is a fundamental part of communicating with data in science, as the variation of our data gives us LOTS of information about our claims from the data.

Great, so we need to visualize our variation in science. If you are like me, and maybe I am just alone here, when it comes to getting kids to visualize the variation things start to fall apart.

Either my students just use the defaults in Excel, neither of which make sense, without having any ideas of what they are actually plotting:

Or follow through the step-by-step instructions that I provide for Excel do it for them correctly (in that the standard deviation of the data is actually what is used as the length of the error bars – NOTE these data only had standard deviation calculated, this can also be done with standard error values):

OR they look at my like I have grown four heads when I even say “error bars.”

Let’s step back to the “Why”

Before we dive into the nitty gritty of actually how to visualize variation (aka how to get them to the middle option above in Excel), let’s think about why we visualize variation in our data.

The simple answer = to understand it better!

Knowing how much our data varies helps us better understand what the pattern is in the data and how confident we are in the pattern we are seeing. Meaning, are the treatment and control groups really different or not, is there actually a pattern over time or not, etc.

Rethinking the “How”

So, let’s use that as our backbone as we think about how to visualize variation. The whole point is to help with interpretation. And if that is the point, are those little vertical lines really helping our students?

What stands out about plotting these data (standard deviation around the mean) as a swath behind the data is that it makes more sense in how to interpret the data. The grey bar helps in your interpretation of the data!

You are able to more intuitively see the range of reading scores over time and how that whole range of scores has been increasing over time. Rather than individual bars positioned vertically at each yearly average (as above), this provides the information (standard deviation) in a way that includes broader context.

When we know that our students struggle out of the gates with understanding variation and what to do with it, maybe spending a bit of time thinking outside the box of how we visualize or variability could help!

The USGS hosts a program called “National Oil and Gas Assessment” through which you can find all sorts of information about oil and gas resources throughout the United States.

Choose a region of the country that appears on the US map to investigate which different types of fossil fuels occur where in the region, as well as when the region was last assessed. Use these data to find more information about your local area as well as other regions of the nation in terms of oil and gas resources. Provide your students with spatial data as you discuss:

different types of oil and gas

the relationship between geological features and the resulting in different forms of fossil fuels

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